Load Balancing Across Models
Routing cheap prompts to small models and hard ones to large models.
Why Route Across Models?
Not every task needs the most powerful (and expensive) model. A simple greeting response does not require GPT-4o. Model routing directs each request to the cheapest model capable of handling it well, reducing cost by 50-90% while maintaining quality where it matters.
Complexity-Based Routing
Classify task complexity before calling the API. Simple tasks go to cheap models; complex tasks go to capable models. A lightweight classifier or heuristics can make this decision quickly.
COMPLEXITY_CLASSIFIER_PROMPT = '''Classify the complexity of this user request.
Return ONLY one word: SIMPLE, MODERATE, or COMPLEX.
SIMPLE: greeting, factual lookup, single-step question, direct answer needed
MODERATE: multi-step explanation, comparison, short analysis, code snippet
COMPLEX: deep analysis, long code generation, reasoning chain, specialized domain
Request: {request}'''
import openai
client_mini = openai.OpenAI(api_key='YOUR_API_KEY')
def classify_complexity(request):
response = client_mini.chat.completions.create(
model='gpt-4o-mini', # always use cheap model for classifier
messages=[{'role': 'user', 'content':
COMPLEXITY_CLASSIFIER_PROMPT.format(request=request)}],
max_tokens=5,
temperature=0
)
label = response.choices[0].message.content.strip().upper()
if label not in ('SIMPLE', 'MODERATE', 'COMPLEX'):
label = 'MODERATE' # safe default
return label
for req in ['Hi', 'Explain quicksort', 'Design a distributed systems architecture']:
print(f'{req[:40]}: {classify_complexity(req)}')All lessons in this course
- Caching Strategies for Prompts
- Batch Processing and Async Execution
- Load Balancing Across Models
- Monitoring and Alerting for Prompt Pipelines